Effects of forward collision warning technology in different pre-crash scenarios

Yue, Lishengsa; Abdel-Aty, Mohamed; Wu, Yina; Ugan, Jorge; Cheng, Yüan

doi:10.1016/j.trf.2020.12.004

Cited by 28 publications

(8 citation statements)

References 42 publications

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“…The visual channel is a suitable way to deliver complex information to the driver, which is mainly continuous information (in-car navigation), low-priority information (in-car media), and warning information (vehicle status information) [ 15 ]. If the information is presented on the center console or dashboard, the human–machine interaction (HMI) will take up the driver’s visual resources, taking his/her eyes off the road and causing visual distraction.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

Huang

2022

Sensors

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V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

Huang

2022

Sensors

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“…Artificial intelligence (AI) is becoming more commonly used in many applications especially autonomous driving (AD) ones such as augmented reality (AR) [ 14 ], automatic emergency braking (AEB) [ 15 ], lane keeping assist (LKA) [ 16 ], active cruise control (ACC) [ 17 ], and crash avoidance (CA) [ 18 ]. Forward collision warning (FCW) [ 19 ] and automatic emergency braking (AEB) are considered as the initial trials to integrate crash avoidance functionality. In FCW, the vehicle is able to only warn the driver that there is an object in front of you, so the driver should take corrective actions.…”

Section: Literature Reviewmentioning

confidence: 99%

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.

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“…In general, on one hand, there are differences in drivers' perceptual preferences in a car-following case: from cautious to aggressive. It leads to diverse timing of braking in the presence of the coming collision cases, hence the need for different FCW trigger timing [19,25]. On the other hand, a fixed warning threshold of critical distance is inappropriate for drivers with varied driving styles as well.…”

Section: Fcw Strategy Based On Fixed Parametersmentioning

confidence: 99%

An Adaptive Multi-Staged Forward Collision Warning System Using a Light Gradient Boosting Machine

Gong

et al. 2022

Information

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The existing forward collision warning (FCW) systems that adopt kinematic or perceptual parameters have some drawbacks in the warning performance because of poor adaptability to the users or ineffectiveness of the warnings. To solve the problems of adaptability, several FCW models have been proposed based on algorithms (machine learning, deep learning). However, there is a lack of consideration for the multi-staged warning to avoid an abrupt warning that may startle or distract the driver. In this study, a light gradient boosting machine (LGBM) was adopted to develop a multi-staged FCW. The proposed model was trained and evaluated on a platform based on a driving simulator by twenty drivers. Through Shapley Additive Explanations (SHAPs), the output of the proposed model was explained. Specifically, the front vehicle acceleration, time-to-collision (TTC), and relative speed were found to strongly affect the warning stages from the proposed model. To evaluate the utility and acceptability of the developed model, it was compared with three existing FCW models in terms of subjective and objective indicators. As a result, a trade-off was found between the utility and user acceptance. Additionally, the comparison study also indicated that the developed model outperformed other previous models due to not only the high accuracy but also the suitable trigger timing for each participant.

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Effects of forward collision warning technology in different pre-crash scenarios

Cited by 28 publications

References 42 publications

Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

An Adaptive Multi-Staged Forward Collision Warning System Using a Light Gradient Boosting Machine

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